Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations

Current Diagnostic Techniques for Pneumonia: A Scoping Review

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

Expanding antibiotic, vaccine, and diagnostics development and access to tackle antimicrobial resistance

The increasing number of bacterial infections globally that do not respond to any available antibiotics indicates a need to invest in-and ensure access to-new antibiotics, vaccines, and diagnostics. The traditional model of drug development, which depends on substantial revenues to motivate investment, is no longer economically viable without push and pull incentives. Moreover, drugs developed through these mechanisms are unlikely to be affordable for all patients in need, particularly in low-income and middle-income countries. New, publicly funded models based on public-private partnerships could support investment in antibiotics and novel alternatives, and lower patients' out-of-pocket costs, making drugs more accessible. Cost reductions can be achieved with public goods, such as clinical trial networks and platform-based quality assurance, manufacturing, and product development support. Preserving antibiotic effectiveness relies on accurate and timely diagnosis; however scaling up diagnostics faces technological, economic, and behavioural challenges. New technologies appeared during the COVID-19 pandemic, but there is a need for a deeper understanding of market, physician, and consumer behaviour to improve the use of diagnostics in patient management. Ensuring sustainable access to antibiotics also requires infection prevention. Vaccines offer the potential to prevent infections from drug-resistant pathogens, but funding for vaccine development has been scarce in this context. The High-Level Meeting of the UN General Assembly in 2024 offers an opportunity to rethink how research and development can be reoriented to serve disease management, prevention, patient access, and antibiotic stewardship.

Evaluation of the Submaximal Treadmill-Based Fitness Test in Six Brachycephalic Breeds-A Follow-Up Study

Brachycephalic obstructive airway syndrome (BOAS) in dogs challenges veterinary surgeons both with a complex clinical picture as well as wide-ranging ways to diagnose the disease, often not easily implemented nor standardised in clinical practice. The assessment of a combination of exercise testing, the occurrence of breathing noises, recovery time, and respiratory effort proved to be an appropriate method to identify Pugs with BOAS. The purpose of this study was to apply an established standardised, submaximal, treadmill-based fitness test for Pugs to other brachycephalic dog breeds. A total of 79 participants, belonging to 6 different brachycephalic breeds, trotted 15 min with an individual comfort speed of 3-7 km/h on a treadmill. Additionally, functional BOAS grading based on respiratory clinical signs before and after exercise was applied. The test was passed if the dogs presented with a BOAS grade of 0 or 1 and their vital parameters recovered to baseline within 15 min after exercise. A total of 68% showed a BOAS grade of 0 or 1 and passed the fitness test. Of the failed participants, 65% failed due to BOAS affectedness, 9% were categorised as not affected by BOAS and failed due to not passing the fitness test only, and 26% showed both failure criteria. The fitness test can be a useful method to identify BOAS-affected dogs in other brachycephalic breeds and to diagnose BOAS in dogs that only show clinical signs under exercise.

Telemedicine in Primary Practice in the Age of the COVID-19 Pandemic-Review

Background and Objectives: In the era of the COVID-19 pandemic, telemedicine, so far underestimated, has gained in value. Currently, telemedicine is not only a telephone or chat consultation, but also the possibility of the remote recording of signals (such as ECG, saturation, and heart rate) or even remote auscultation of the lungs. The objective of this review article is to present a potential role for, and disseminate knowledge of, telemedicine during the COVID-19 pandemic. Material and Methods: In order to analyze the research material in accordance with PRISMA guidelines, a systematic search of the ScienceDirect, Web of Science, and PubMed databases was conducted. Out of the total number of 363 papers identified, 22 original articles were subjected to analysis. Results: This article presents the possibilities of remote patient registration, which contributes to an improvement in remote diagnostics and diagnoses. Conclusions: Telemedicine is, although not always and not by everyone, an accepted form of providing medical services. It cannot replace direct patient-doctor contact, but it can undoubtedly contribute to accelerating diagnoses and improving their quality at a distance.

Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: A systematic review

BACKGROUND

Acute respiratory diseases are a leading cause of morbidity and mortality in children. Cough is a common symptom of acute respiratory diseases and the sound of cough can be indicative of the respiratory disease. However, cough sound assessment in routine clinical practice is limited to human perception and the skills of the clinician. Objective cough sound evaluation has the potential to aid clinicians in acute respiratory disease diagnosis. In this systematic review, we assess and summarize the predictive ability of machine learning algorithms in analyzing cough sounds of acute respiratory diseases in the pediatric population.

METHOD

Our systematic search of the Scopus, Medline, and Embase databases on 25 January 2023 identified six articles meeting the inclusion criteria. Quality assessment of the included studies was performed using the checklist for the assessment of medical artificial intelligence.

RESULTS

Our analysis shows variability in the input to the machine learning algorithms, such as the use of various cough sound features and combining cough sound features with clinical features. The use of the machine learning algorithms also varies from conventional algorithms, such as logistic regression and support vector machine, to deep learning techniques, such as convolutional neural networks. The classification accuracy for the detection of bronchiolitis, croup, pertussis, and pneumonia across five articles is in the range of 82-96%. However, a significant drop is observed in the detection accuracy for bronchiolitis and pneumonia in the remaining article.

CONCLUSION

The number of articles is limited but, in general, the predictive ability of cough sound classification algorithms in childhood acute respiratory diseases shows promise.

A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study

Objective: Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.Methods: We conducted a double-blind, prospective, diagnostic accuracy study comparing the algorithm with expert clinical opinion and formal lung function testing. Results: One hundred nineteen participants >12 years with a physician-diagnosed history of asthma were recruited from a hospital in Perth, Western Australia: 46 with clinically confirmed asthma exacerbations, 73 with controlled asthma. The groups were similar in median age (54yr versus 60yr, p=0.72) and sex (female 76% versus 70%, p=0.5). The algorithm's positive percent agreement (PPA) with the expert clinical diagnosis of asthma exacerbations was 89% [95% CI: 76%, 96%]. The negative percent agreement (NPA) was 84% [95% CI: 73%, 91%]. The algorithm's performance for asthma exacerbations diagnosis exceeded its performance as a detector of patient-reported wheeze (sensitivity, 63.7%). Patient-reported wheeze in isolation was an insensitive marker of asthma exacerbations (PPA=53.8%, NPA=49%). Conclusions: Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing. This method could improve the accuracy of telehealth consultations and might be helpful in Asthma Action Plans and patient-initiated therapy.

Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review

Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.

Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report

Spirometer measurements can reflect cough strength but might not be routinely available for patients with severe neurological or medical conditions. A digital device that can record and help track abnormal cough sound changes serially in a noninvasive but reliable manner would be beneficial for monitoring such individuals. This report includes two cases of respiratory distress whose cough changes were monitored via assessments performed using recordings made with a digital device. The cough sounds were recorded using an iPad (Apple, Cupertino, CA, USA) through an embedded microphone. Cough sounds were recorded at the bedside, with no additional special equipment. The two patients were able to complete the recordings with no complications. The maximum root mean square values obtained from the cough sounds were significantly reduced when both cases were diagnosed with aspiration pneumonia. In contrast, higher values became apparent when the patients demonstrated a less severe status. Based on an analysis of our two cases, the patients’ cough sounds recorded with a commercial digital device show promise as potential digital biomarkers that may reflect aspiration risk related to attenuated cough force. Serial monitoring aided the decision making to resume oral feeding. Future studies should further explore the clinical utility of this technique.

Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments

Background: Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments. Objective: To evaluate the diagnostic accuracy of emergency department clinicians in diagnosing acute childhood respiratory diseases, as compared with expert panel consensus (reference standard). Methods: Prospective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience sample of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records. Results: For 620 participants, the sensitivity and specificity (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (64.9 [54.6, 74.4], 91.0 [88.2, 93.3]), croup (76.8 [66.2, 85.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.6 [83.1, 89.6], 92.9 [87.6, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (63·9 [50.6, 75·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9 and 14.5% of children were inappropriately prescribed based on their eventual diagnosis. Conclusion: In well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

Comprehensive management of acute pulmonary embolism in primary care using telemedicine in the COVID-era

A healthy, active woman in her 70s reported intermittent exertional dyspnoea for 2 months, notable during frequent open-water swimming. Symptoms were similar to an episode of travel-provoked pulmonary embolism 3 years prior. She denied chest pain, cough, fever, extremity complaints and symptoms at rest. Due to the COVID-19 pandemic, her healthcare system was using secure telemedicine to evaluate non-critical complaints. During the initial video visit, she appeared well, conversing normally without laboured breathing. An elevated serum D-dimer prompted CT pulmonary angiography, which identified acute lobar pulmonary embolism. After haematology consultation and telephone conversation with the patient, her physician prescribed rivaroxaban. Her symptoms rapidly improved. She had an uneventful course and is continuing anticoagulation indefinitely. The pandemic has increased the application of telemedicine for acute care complaints. This case illustrates its safe and effective use for comprehensive management of acute pulmonary embolism in the primary care setting.